Development and Deployment of DeepSeek-Based Applications in Healthcare: A Chinese Perspective

Artificial intelligence (AI) is already showing enormous potential in the healthcare sector. Generative AI, particularly, is accelerating the sector's digital transformation by delivering intelligent decision support, automated diagnosis, and optimized resource allocation. DeepSeek-R1, a large-language model with a strong performance-to-cost ratio, has gained popularity as a foundation model in Chinese hospitals. However, the deployment of generative AI remains challenging, and hospitals continue to lack clear guidance on how to select among deployment architectures and how to balance computational demand with cost. Deeper, data-driven analysis is therefore warranted to inform future roll-outs.

This study surveyed AI deployment across Chinese hospitals, with a focus on DeepSeek's potential applications under national policies. Data were collected from the top 20 hospitals, regional centers, and township hospitals via official WeChat platforms. The survey examined deployment strategies, model versions, and platform choices, while keeping in account hospital needs, data resources, and technological-economic decisions.

The study highlights DeepSeek's impact on diagnostic accuracy, personalized treatment, medical documentation automation, and resource management optimization. Among the 17 surveyed hospitals, 6 hospitals employed detailed model versions, 5 used the 671B model, and 1 used the 32B version. Among 10 hospitals of different levels, 2 selected the 671B, 3 selected the 70B, and 4 selected the 32B model. All hospitals preferred local deployment. Different needs and applications were observed across the studied hospitals.

Selection of the right AI model requires balancing computational power with cost. Larger models offer higher accuracy, but incur higher costs, whereas distilled models suit smaller hospitals with fewer resources. Future development should therefore focus on selecting deployment strategies based on the hospital size while addressing data quality disparities to bridge the regional healthcare gaps. As such, coordination among government, hospitals, and doctors is crucial for supporting smarter healthcare transitions.